
import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:int	,  a						:UNetaDModel	,  a						:UNetaDModel	,  a						:DDPMScheduler	,  a						:List[Any]	,  )						->					Optional[int]:
								super().__init__()
								__UpperCamelCase      :  List[Any]       =   value_function
								__UpperCamelCase      :  Tuple       =   unet
								__UpperCamelCase      :  Optional[Any]       =   scheduler
								__UpperCamelCase      :  Dict       =   env
								__UpperCamelCase      :  List[Any]       =   env.get_dataset()
								__UpperCamelCase      :  Any       =   {}
								for key in self.data.keys():
															try:
																						__UpperCamelCase      :  Tuple       =   self.data[key].mean()
															except:  # noqa: E722
																						pass
								__UpperCamelCase      :  List[Any]       =   {}
								for key in self.data.keys():
															try:
																						__UpperCamelCase      :  Tuple       =   self.data[key].std()
															except:  # noqa: E722
																						pass
								__UpperCamelCase      :  Optional[int]       =   env.observation_space.shape[0]
								__UpperCamelCase      :  List[Any]       =   env.action_space.shape[0]







	def   _lowerCamelCase							(   self						:int	,  a						:List[Any]	,  a						:int		)						->					int:
								return (x_in - self.means[key]) / self.stds[key]







	def   _lowerCamelCase							(   self						:int	,  a						:int	,  a						:Tuple		)						->					int:
								return x_in * self.stds[key] + self.means[key]







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:str		)						->					Optional[int]:
								if type(a		) is dict:
															return {k: self.to_torch(a		) for k, v in x_in.items()}
								elif torch.is_tensor(a		):
															return x_in.to(self.unet.device		)
								return torch.tensor(a	,  device=self.unet.device		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:List[Any]	,  a						:Tuple	,  a						:Dict		)						->					Tuple:
								for key, val in cond.items():
															__UpperCamelCase      :  List[Any]       =   val.clone()
								return x_in







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:str	,  a						:Union[str, Any]	,  a						:str	,  a						:Any		)						->					List[str]:
								__UpperCamelCase      :  List[Any]       =   x.shape[0]
								__UpperCamelCase      :  Optional[Any]       =   None
								for i in tqdm.tqdm(self.scheduler.timesteps		):
															# create batch of timesteps to pass into model
															__UpperCamelCase      :  int       =   torch.full((batch_size,)	,  a	,  device=self.unet.device	,  dtype=torch.long		)
															for _ in range(a		):
																						with torch.enable_grad():
																													x.requires_grad_()

																													# permute to match dimension for pre-trained models
																													__UpperCamelCase      :  Union[str, Any]       =   self.value_function(x.permute(0	,  2	,  1		)	,  a		).sample
																													__UpperCamelCase      :  Dict       =   torch.autograd.grad([y.sum()]	,  [x]		)[0]

																													__UpperCamelCase      :  List[Any]       =   self.scheduler._get_variance(a		)
																													__UpperCamelCase      :  Optional[int]       =   torch.exp(0.5 * posterior_variance		)
																													__UpperCamelCase      :  Optional[Any]       =   model_std * grad

																						__UpperCamelCase      :  int       =   0
																						__UpperCamelCase      :  Tuple       =   x.detach()
																						__UpperCamelCase      :  List[Any]       =   x + scale * grad
																						__UpperCamelCase      :  Any       =   self.reset_xa(a	,  a	,  self.action_dim		)

															__UpperCamelCase      :  List[str]       =   self.unet(x.permute(0	,  2	,  1		)	,  a		).sample.permute(0	,  2	,  1		)

															# TODO: verify deprecation of this kwarg
															__UpperCamelCase      :  Optional[Any]       =   self.scheduler.step(a	,  a	,  a	,  predict_epsilon=a		)["prev_sample"]

															# apply conditions to the trajectory (set the initial state)
															__UpperCamelCase      :  Optional[Any]       =   self.reset_xa(a	,  a	,  self.action_dim		)
															__UpperCamelCase      :  List[str]       =   self.to_torch(a		)
								return x, y







	def __call__(   self						:List[Any]	,  a						:Union[str, Any]	,  a						:str=6_4	,  a						:int=3_2	,  a						:str=2	,  a						:Optional[int]=0.1		)						->					Tuple:
								# normalize the observations and create  batch dimension
								__UpperCamelCase      :  Dict       =   self.normalize(a	,  "observations"		)
								__UpperCamelCase      :  Optional[int]       =   obs[None].repeat(a	,  axis=0		)

								__UpperCamelCase      :  Optional[int]       =   {0: self.to_torch(a		)}
								__UpperCamelCase      :  Optional[Any]       =   (batch_size, planning_horizon, self.state_dim + self.action_dim)

								# generate initial noise and apply our conditions (to make the trajectories start at current state)
								__UpperCamelCase      :  Optional[int]       =   randn_tensor(a	,  device=self.unet.device		)
								__UpperCamelCase      :  Dict       =   self.reset_xa(a	,  a	,  self.action_dim		)
								__UpperCamelCase      :  Any       =   self.to_torch(a		)

								# run the diffusion process
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[Any]       =   self.run_diffusion(a	,  a	,  a	,  a		)

								# sort output trajectories by value
								__UpperCamelCase      :  Optional[int]       =   y.argsort(0	,  descending=a		).squeeze()
								__UpperCamelCase      :  Optional[int]       =   x[sorted_idx]
								__UpperCamelCase      :  str       =   sorted_values[:, :, : self.action_dim]
								__UpperCamelCase      :  int       =   actions.detach().cpu().numpy()
								__UpperCamelCase      :  List[str]       =   self.de_normalize(a	,  key="actions"		)

								# select the action with the highest value
								if y is not None:
															__UpperCamelCase      :  int       =   0
								else:
															# if we didn't run value guiding, select a random action
															__UpperCamelCase      :  Optional[int]       =   np.random.randint(0	,  a		)

								__UpperCamelCase      :  List[Any]       =   denorm_actions[selected_index, 0]
								return denorm_actions

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase    :   List[str]						=			{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			[
		    'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'GPTBigCodeForSequenceClassification',
		    'GPTBigCodeForTokenClassification',
		    'GPTBigCodeForCausalLM',
		    'GPTBigCodeModel',
		    'GPTBigCodePreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_bigcode import (
				    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTBigCodeForCausalLM,
				    GPTBigCodeForSequenceClassification,
				    GPTBigCodeForTokenClassification,
				    GPTBigCodeModel,
				    GPTBigCodePreTrainedModel,
				)


else:
		import sys

		lowercase    :   Tuple						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)